Category Archives: Interoperability

New #FHIR Vital Signs Profile

Over on the official FHR product blog, I just announced a new release. I wanted to expand on one of the features in the new version here

A new profile to describe vital signs (note: this is proposed as mandatory to enable better data sharing)

One of the emerging themes in many countries is sharing data with patients. And one of the broad set of things called ‘health data’ that is easiest to share is what is loosely called ‘vital signs’. It’s simple data, it’s easy to share with the patients, and we’re starting to see monitoring devices available in mainstream consumer technology. But it’s more than just patients that care – vital signs data is widely shared through the healthcare provision system, and there’s lots of interesting decision support and surveillance that can usefully be done with them.

But if every different part of the healthcare system, or different jurisdictions, represent basic vital signs differently, there’ll be no way to easily share decision support and survelliance systems, nor will patients be able to share their healthcare data with common data management tools – which are cross-jurisdictional (e.g. things like healthkit/carekit).  With this in mind, we’ve defined a vital signs profile in the new draft of FHIR, and said that all vital signs must conform to it. It doesn’t say much:

  • The common vital signs must be marked with a common generic LOINC code, in addition to whatever other codes you want to give them
  • There must be a value or a data absent reason
  • There must be a subject for the observations
  • Systolic/Diastolic must be represented using components
  • The units must be a particular UCUM unit

This is as minimal a floor as we can get: defining a common way to recognize a vital sign measurement, and a common unit for them. None of this restricts what else can be done, so this is really very minimal.

For FHIR, this is a very gentle step towards being proscriptive about how healthcare data is represented. But even this looks likely to generate fierce debate within the implementer community, some of whom don’t see the data sharing need as particularly important or near in the future. I’m writing this post to draw everyone’s attention to this, to ensure we get a good wide debate about this idea.

Note: it’s a proposal, in a candidate standard. It has to get through ballot before it’s actually mandatory.

 

Patient Matching on #FHIR Event at HIMSS

In a couple of weeks I’m off to HIMSS at Los Vegas. I’m certainly going to be busy while I’m there (If you’re hoping to talk to me, it would be best to email me to set up a time). Before HIMSS, there’s several satellite events:

  • Saturday: Health Informatics on FHIR: Opportunities in the New Age of Interoperability (IEEE)
  • Sunday: Patient Matching on FHIR event (HIMSS)
  • Monday: First joint meeting between HEART/UMA & FHIR/SMART teams – if you want to attend this meeting, let me know by email (there’s a couple of places still open)

About the Sunday meeting, quoting from the announcement:

Previous work included a Patient Testing Matching Event on this idea was developed at an event in Cleveland, OH on August 14th, 2015 at the HIMSS Innovation Center.  The event covered a tutorial on FHIR along with sessions on patient matching.  A key takeaway from the event was that the healthcare community can advance interoperability by working on a standard Application Programming Interface (API) for master patient index software, commonly used to facilitate patient data matching.

In fulfillment of this vision, we are hosting this second Patient Matching on FHIR Workshop in conjunction with the HIMSS 16 Annual Conference in Las Vegas.  We invite:

─         Algorithm vendors
─         EMR vendors,
─         Developers and standards experts
─         All interested parties

So, here’s passing on the invitation – see you there!

ps. I’ll pass on information about the IEEE event when I get a link.

Language Localization in #FHIR

Several people have asked about the degree of language localization in FHIR, and what language customizations are available.

Specification

The specification itself is published and balloted in English (US). All of our focus is on getting that one language correct.

There are a couple of projects to translate this to other languages: Russian  and  Japanese, but neither of these have gone very far, and to do it properly would be a massive task. We would consider tooling in the core build to make this easier (well, possible) but it’s not a focus for us.

One consequence of the way the specification works is that the element names (in either JSON or XML) are in english. We think that’s ok because they are only references into the specification; they’re never meant to be meaningful to any one other than a user of the specification itself.

Codes & Elements

What is interesting to us is publishing alternate language phrases to describe the codes defined in FHIR code systems, and the elements defined in FHIR resources. These are text phrases that do appear to the end-user, so it’s meaningful to provide these.

A Code System defined in a value set has a set of designations for each code, and these designations have a language on them:

code system

Not only have we defined this structure, we’ve managed to provide Dutch and German translations for the HL7 v2 tables. However, at present, these are all we have. We have the tooling to add more, it’s just a question of the HL7 Affiliates deciding to contribute the content.

For the elements (fields) defined as part of the resources, we’d happily add translations of these too, though there’s no native support for this in the StructureDefinition, so it would need an extension. However no one has contributed content like this yet.

It’s not necessary to do this as part of the specification (though doing it there provides the most visibility and distribution), though we haven’t defined any formal structure for a language pack. If there’s interest, this is something we could do in the future.

Useful Phrases

There’s also a source file that has a set of common phrases – particularly error phrases (which do get displayed to the user) – in multiple languages. This is available as part of the specification. Some of the public test servers use these messages when responding to requests.

Note that we’ve been asked to allow this to be authored through a web site that does translations – we’d be happy to do support this, if we found one that had an acceptable license, could be used for free, and had an API so we could download the latest translations as part of the build (I haven’t seen any service that has those features)

Multi-language support in resources

There’s no explicit support for multi-language representation in resources other than for value set. Our experience is that while mixed language content occurs occasionally, it’s usually done informally, in the narrative, and rarely formally tracked. So far, we’ve been happy to leave that as an extension, though there is ongoing discussion about that.

The one place that we know of language being commonly tracked as part of a resource is in document references, with a set of form letters (e.g. procedure preparation notes) in multiple languages. For this reason, the DocumentReference resource as a language for the document it refers to (content.language).

Question: #FHIR and patient generated data

Question:

With the increase in device usage and general consumer-centric health sites (e.g. myfitnesspal, Healthvault, Sharecare) coupled with the adoption of FHIR, it seems like it is becoming more and more common for a consumer to be able to provide the ability to share their data with a health system. The question I have lies in the intersection of self-reported data and the clinical record.

How are health systems and vendors handling the exchange (really, ingest) of self-reported data?

An easy example is something along the lines of I report my height as 5’10” and my weight as 175 in MyFitnessPal and I now want to share all my diet and bio  data with my provider.  What happens to the height and weight?  Does it get stored as a note?  As some other data point?  Obviously, with FHIR, the standard for transferring become easier, however, I’m curious what it looks like on the receiving end. A more complicated example might the usage of codifying an intake form.  How would i take a data value like “do you smoke” and incorporate that into the EHR?  Does it get stored in the actual clinical record or again, as a note?  If not in the clinical system, how do I report (a la MU) on this data point.

Answer

Well, FHIR enables this kind of exchange, but as you say, what’s actually happening in this regard? Well, as you say, it’s more a policy / procedure question, so I have no idea (though the draft MU Stage 3 rule would give credit for this as data from so-called “non-clinical” sources). But what I can do is ask the experts – leads on both the vendor and provider side. So that’s what I did, and here’s some of their comments.

From a major vendor integration lead:

For us, at least, the simplest answer is the always-satisfying “it’s complicated.”

Data generally falls into one of the following buckets:

  1. Data that requires no validation: data that is subjective. PHQ-2/9.
  2. Data that requires no validation (2): data that comes directly from devices/Healthkit/Google Fit.
  3. Data that requires minimal validation: data that is mostly subjective but that a clinician might want to validate that the patient understood the scope of the question – ADLs, pain score, family history, HPI, etc.
  4. Data that requires validation: typically, allergies/problems/meds/immunizations; that is, data that contributes to decision support and/or the physician-authored medical record.
  5. Data that is purely informational and that is not stored discretely.

Depending on what we are capturing, there are different confirmation paths.

Something like height and weight would likely file into (e). Height and weight are (a) already captured as a part of typical OP flow and (b) crucially important to patient safety (weight-based dosing), so it’s unlikely that a physician would favor a patient-reported height/weight over a clinic-recorded value.

That said, a patient with CHF who reports a weight gain > 2lb overnight will likely trigger an alert, and the trend remains important. But the patient-reported value is unlikely to replace a clinic-recorded value.

John Halamka contributed this BIDMC Patient Data Recommendation, along with a presentation explaining it. Here’s a very brief extract:

Purpose: To define and provide a process to incorporate Patient Generated Health Data into clinical practice

Clinicians may use PGHD to guide treatment decisions, similarly to how they would use data collected and recorded in traditional clinical settings. Judgment should be exercised when electronic data from consumer health technologies are discordant with other data

Thanks John – this is exactly the kind of information that is good to share widely.

Question: Solutions for synchronization between multiple HL7-repositories?

Question:

In the area of using HL7 for patient record storage, there are use cases to involve various sources of patient information who are involved in the care for one patient. For these people, we need to be able to offer a synchronization between multiple HL7-repositories. Are there any implementations of a synchronization engine between HL7 repositories?

Answer:

There is no single product that provides a solution like this. Typically, a working solution like this involves a great deal of custom business logic, and such solutions are usually solved using a mixture of interface engines, scripting, and bespoke code and services developed in some programming language of choice. See Why use an interface engine?

This is a common problem that has been solved more than once in a variety of ways with a myriad of products.

Here’s an overview of the challenge:

If by synchronization we mean just “replication” from A to B, then A needs to be able to send and B needs to receive messages or service calls. If by synchronization we mean two-way “symmetric” synchronization then you have to add logic to prevent “‘rattling” (where the same event gets triggered back and forth). An integration engine can provide the transformations between DB records and messages, but in general the concept codes and identifiers must still be reconciled between the systems.

For codes, an “interlingua” like SNOMED, LOINC, etc. is helpful if one or both of the systems uses local codes. The participants may implement translations (lookups) to map to the other participant or to the interlingua (it acts as the mediating correlator) The interface engine can call services, or perform the needed lookups. “Semantic” mapping incorporates extra logic for mapping concepts that are divided into their aspects (like LOINC, body system, substance, property, units, etc. Naturally if all participants actually support the interlingua natively the problem goes away. For identifiers, a correlating EMPI at each end can find-or-register patients based on matching rules. If a simplistic matching rule is sufficient and the receiving repository is just a database, then the integration engine alone could map the incoming demographic profile to a query against the patients table and look up the target patient – and add one if it’s new.

But if the target repository has numerous patients, with probabilistic matching rules (to maximize the rate of unattended matches, i.e. not bringing a human registrar into the loop to do merges), then the receiving system should implement a service of some kind (using HL7/OMG IXS standard, OMG PIDS (ref?), or FHIR), and the integration engine can translate the incoming demographic into a find-or-register call to that service. Such a project will of course require some analysis and configuration, but with most interface engines, there will be no need for conventional programming. Rather, you have (or make) trees that describe the message segments, tables, or service calls, and then you map (drag/drop) the corresponding elements from sources to targets.

An MDM or EMPI product worth its salt will implement a probabilistic matching engine and implement a web-callable interface (SOAP or REST) as described. If the participants are organizationally inside the same larger entity (a provider health system), then the larger organization may implement a mediating correlator just like the interlingua for terminology. The “correlating” EMPI assigns master identifiers in response to incoming feeds (carrying local ids) from source systems; Then that EMPI can service “get corresponding ids” requests to support the scenario you describe. An even tighter integration results if one or both participants actually uses that “master” id domain as its patient identifiers.

Here’s some example projects along these lines:

  • dbMotion created a solution that would allow a clinical workstation to access information about a common patient from multiple independent EMRs. It accomplished this by placing an adapter on top of EHR that exposed its data content in a common format (based upon the RIM) that their workstation application was able to query and merge the patient data from all the EMR into a single desktop view. The actual data in the source EHR were never modified in any way. This was implemented in Israel and then replicated in the US one RHIO at a time. (Note: dbMotion has since been acquired by Allscripts)
  • California State Immunization created a solution that facilitated synchronization of patient immunization history across the nine different immunization registries operating within the state. The solution was based upon a family of HL7 v2 messages that enabled each registry to request patient detail from another and use the query result to update its own record. This solution was eventually replaced by converting all the registries to a common technical platform and then creating a central instance of the system that served all of the regional registries in common (so synchronization was no longer an issue now that there was a single database of record, which is much simpler to maintain).
  • LA County IDR is an architecture put in place in Los Angles County to integrate data from the 19+ public health information system both as a means of creating a master database that could be used for synchronization and could be used as a single source to feed data analytics. The Integrated Data Repository was built using a design that was first envisioned as part of the CDC PHIN project. The IDR is a component of the CDC’s National Electronic Disease Surveillance System (NEDSS) implemented in at least 16 state health departments.

The following people helped with this answer: Dave Shaver, Abdul Malik Shakir, Jon Farmer

Profiles and Exceptions to the Rules

One of the key constructs in FHIR is a “profile”. A profile is a statement of how FHIR resources are used for a particular solution – or, how they should be used. The FHIR resources are a general purpose construct, and you can do kind of general purpose things with them, such as store the data in a PHR, and do generally useful display of a clinical record etc.

But if you’re going to do something more specific, then you need to be specific about the contents. Perhaps, for instance, you’re going to write a decision support module that takes in ongoing glucose and HBA1c measurements, and keeps the patient informed about how well they are controlling their diabetes. In order for a patient or an institution to use that decision support module well, the author of the module is going to have to be clear about what are acceptable input measurements – and it’s very likely, unfortunately, that the answer is ‘not all of them’. Conversely, if the clinical record system is going to allow it’s users to hook up decision support modules like this, it’s going to have to be clear about what kind of glucose measurements it might feed to the decision support system.

If both the decision support system and the clinical records system produce profiles, a system administrator might even able to get an automated comparison to see whether they’re compatible. At least, that’s where we’d like to end up.

For now, however, let’s just consider the rules themselves. A clinical record system might find itself in this situation:

  • We can provide a stream of glucose measurements to the decision support system
  • They’ll come from several sources – labs, point of care testing devices, inpatient monitoring systems, and wearables
  • There’s usually one or more intermediary systems between the actual glucose measurement, and the clinical record system (diagnostic systems, bedside care systems, home health systems – this is a rapidly changing space)
  • Each measurement will have one of a few LOINC codes (say, 39480-9: Glucose [Moles/volume] in Venous blood, 41652-9: Glucose [Mass/volume] in Venous blood,
    14743-9: Glucose [Moles/volume] in Capillary blood by Glucometer)
  • the units of measure will be mg/dL or mmol/L
  • there’ll be a numerical value, perhaps with a greater than or less than comparator (e.g. >45mmol/L)

So you can prepare a FHIR profile that says this one way or another. And then a decision support engine can have a feel for what kind of data it might get, and make sure it can handle it all appropriately.

So that’s all fine. But…

Eventually, the integration engineers that actually bring the data into the system discover – by looking at rejected messages (usually) – 1 in a million inbound glucose measurements from the lab contain a text message instead of a numerical value. The message might be “Glucose value to high to determine”. Now what? From a clinical safety perspective, it’s almost certain that the integration engineers won’t replace “too high to determine’ with a “>N” where N is some arbitrarily chosen number – there’s no number they can choose that isn’t wrong. And they won’t be able to get the source system to change their interface either – that would have other knock-on effects for other customers / partners of the source system. Nor can they drop the data from the clinical record – it’s the actual test result. So they’ll find a way to inject that value into the system.

Btw- aside – some of the things that go in this string value could go in Observation.dataAbsentReason, but they’re not coded, and it’s not possible to confidently decide which are missing reasons, and which are ‘text values’. So dataAbsentReason isn’t a solution to this case, though it’s always relevant.

Now the system contains data that doesn’t conform to the profile it claimed to use. What should happen?

  1. The system hides the data and doesn’t let the decision support system see it
  2. The system changes it’s profile to say that it might also send text instead of a number
  3. The system exposes the non-conformant data to the decision support system, but flags that it’s not valid according to it’s own declarations

Neither of these is palatable. I assume that #1 isn’t possible, at least, not as a blanket policy. There’s going to be some clinical safety reason why the value has to be passed on, just the same as the integration engineers passed it on in the first place, so that there’re not liable.

Option #2 is a good system/programmer choice – just tell me what you’re going to do, and don’t beat around the bush. And the system can do this – it can revise the statement ‘there’ll be a numerical value’ to something like ‘there’ll be a numerical value, or some text’. At least this is clear.

Only it creates a problem – now, the consumer of the data knows that they might get a number, or a string. But why might the get a string? what does it mean? Someone does know, somewhere, that the string option is used 1 in a million times, but there’s no way (currently, at least) to say this in the profile – it just says what’s possible, not what’s good, or ideal, or common. If you start considering the impact of data quality on every element – which you’re going to have to do – then you’re going to end up with a profile that’s technically correct but quite non-comunicative about what the data might be, nor one that provides any guidance as to what it should be, so that implementers know what they should do. (and observationally, if you say that it can be a string, then, hey, that’s what the integration engineers will do to, because it’s quicker….)

That’s what leads to the question about option #3: maybe the best thing to do is to leave the profile saying what’s ideal, what’s intended, and let systems flag non-conforming resources with a tag, or wrong elements with an extension? Then the consumer of the information can always check, and ignore it if they want to.

That is, if they know about the flag, and remember. Which means we’d need to define it globally, and the standard itself would have to tell people to check for data that isn’t consistent with it’s claims… and then we’d have to add overrides to say that some rules actually mean what they say, as opposed to not actually meaning that…. it all sounds really messy to me.

Perhaps, the right way to handle this is to have ideal and actual profiles? That would mean an extension to the Conformance resource so you could specify both – but already the interplay between system and use case profiles is not well understood.

I think this area needs further research.

p.s. There’s more than some passing similarity between this case and the game of ‘hot potato‘ I used to play as a kid: ‘who’s going to do have to do something about this bad data’.

#FHIR, RDF, and JSON-LD

FHIR doesn’t use JSON-LD. Some people are pretty critical of that:

It’s a pity hasn’t been made compatible. Enormous missed opportunity for interop & simplicity.

That was from David Metcalfe by Twitter. The outcome of our exchange after that was that David came down to Melbourne from Sydney to spend a few hours with me discussing FHIR, rdf, and json-ld (I was pretty amazed at that, thanks David).

So I’ve spent a few weeks investigating this, and the upshot is, I don’t think that FHIR should use json-ld.

Linked Data

It’s not that the FHIR team doesn’t believe in linked data – we do, passionately. From the beginning, we designed FHIR around the concept of linked data – the namespace we use is http://hl7.org/fhir and that resolves right to the spec. Wherever we can, we ensure that the names we use in that namespace are resolvable and meaningful on the hl7.org server (though I see that recent changes in the hosting arrangements have somehow broken some of these links). The FHIR spec, as a RESTful API, imposes a linked data framework on all implementations.

It’s just a framework though – using the framework to do fully linked data requires a set of additional behaviours that we don’t make implementers do. Not all FHIR implementers care about linked data – many don’t, and the more closely linked to institutional healthcare, the more important specific trading partner agreements become. One of the major attractions FHIR has in the healthcare space is that it can serve as a common format across the system, so supporting these kind of implementers is critical to the FHIR project. Hence, we like linked data, we encourage it’s use, but it’s not mandatory.

JSON-LD

This is where json-ld comes into the picture – the idea is that you mark up you json with a some lightweight links, which link the information in the json representation to it’s formal definitions so that the data and it’s context can be easily understood outside the specific trading partner agreements.

We like that idea. It’s a core notion for what we’re doing in FHIR, so it sounds like that’s how we should do things. Unfortunately, for a variety of reasons, it appears that it doesn’t make sense for us to use json-ld.

RDF

Many of the reasons that json-ld is not a good fit for FHIR arise because of RDF, which sits in the background of json-ld. From the JSON-LD spec:

JSON-LD is designed to be usable directly as JSON, with no knowledge of RDF. It is also designed to be usable as RDF, if desired, for use with other Linked Data technologies like SPARQL.

FHIR has never had an RDF representation, and it’s a common feature request. There’s a group of experts looking at RDF for FHIR (technically, the ITS WGM RDF project) and so we’ve finally got around to defining RDF for FHIR. Note that this page is editors draft for committee discussion – there’s some substantial open issues. We’re keen, though, for people to test this, particular the generated RDF definitions.

RDF for FHIR has 2 core parts:

  • An RDF based definition of the specification itself – the class definitions of the resources, the vocabulary definitions, and all the mappings and definitions associated with them
  • A method for representing instances of resources as RDF

Those two things are closely related – the instances are represented in terms of the class model defined in the base RDF, and the base RDF uses the instance representation in a variety of ways.

Working through the process of defining the RDF representation for FHIR has exposed a number of issues for an RDF representation of FHIR resources:

  • Dealing with missing data: a number of FHIR elements have a default value, or, instead, have an explicit meaning for a missing element (e.g. MedicationAdministration: if there is no “notGiven” flag, then the medication as given as stated). In the RDF world (well, the ontology world built on top of it) you can’t reason about missing data, since it’s missing. So an RDF representation for FHIR has to make the meaning explicit by requiring default values to be explicit, and providing positive assertions about some missing elements
  • Order does matter, and RDF doesn’t have a good solution for it. This is an open issue, but one that can’t be ducked
  • It’s much more efficient, in RDF, to change the way extensions are represented; in XML and JSON, being hierarchies (and, in XML, and ordered one), having a manifest where mandatory extension metadata (url, type) is represented is painful, and, for schema reasons, difficult. So this data is inlined into the extension representation. In RDF, however, being triple based with an inferred graph, it’s much more effective to separate these into a manifest
  • for a variety of operational reasons, ‘concepts’ – references to other resources or knowledge in ontologies such as LOINC or SNOMED CT – are done indirectly. For Coding, for instance, rather than simply having a URL that refers directly to the concept, we have system + code + version. If you want to reason about the concept that represents, it has to be mapped to the concept directly. That level of indirection exists for good operational reasons, and we couldn’t take it out. However the mapping process isn’t trivial

In the FHIR framework, RDF is another representation like XML and JSON. Client’s can ask servers to return resources or sets of resources using RDF instead of JSON or XML. Servers or clients that convert between XML/JSON and RDF will have to handle these issues – and the core reference implementations that many clients and servers choose to use will support RDF natively (at least, that’s what the respective RI maintainers intend to do).

Why not to use JSON-LD

So, back to json-ld. The fundamental notion of json-ld is that you can add context references to your json, and then the context points to a conversion template that defines how to convert the json to RDF.

From a FHIR viewpoint, then, either the definition of the conversion process is sophisticated enough to handle the kinds of issues discussed above, or you have to compromise either the JSON or the RDF or both.

And the JSON –> RDF conversion defined by the JSON-LD specification is pretty simple. In fact, we don’t even get to the issues discussed above before we run into a problem. The most basic problem has to do with names – JSON-LD assumes that everywhere a JSON property name is used, it has the same meaning. So, take this snippet of JSON:

{ 
  "person" : {
    "dob" : "1975-01-01",
    "name" : {
      "family" : "Smith",
      "given" : "Joe"
    }
  },
  "organization" : {
     "name" : "Acme"
  } 
}

Here, the json property ‘name’ is used in 1 or 2 different ways. It depends on what you mean by ‘meaning’. Both properties associate a human usable label to a concept, one that humans use in conversation to identify an entity, though it’s ambiguous. That’s the same meaning in both cases. However the semantic details of the label – meaning at a higher level – are quite different. Organizations don’t get given names, family names, don’t change their names when they get married or have a gender change. And humans don’t get merged into other humans, or have their names changed for marketing reasons (well, mostly 😉 ).

JSON-LD assumes that anywhere that a property ‘name’ appears, it has the same RDF definition. So that snippet above can’t be converted to json-ld by a simple addition of a json-ld @context. Instead, you would have to rename the name properties to ‘personName’ and ‘organizationName’ or similar. In FHIR, however, we’ve worked on the widely accepted practice that names are scoped by their type (that’s what types do). The specification defines around 2200 elements, with about 1500 names – so 700 of them or so use names that other elements also use. We’re not going to rename all these elements to pre-coordinate their type context into the property name. (Note that JSON-LD discussed supporting having names scoped by context – but this is an ‘outstanding’ request that seems unlikely to get adopted anytime soon).

Beyond that, the other issues are not addressed by json-ld, and unlikely to be soon. Here’s what JSON-LD says about ordered arrays:

Since graphs do not describe ordering for links between nodes, arrays in JSON-LD do not provide an ordering of the contained elements by default. This is exactly the opposite from regular JSON arrays, which are ordered by default

and

List of lists in the form of list objects are not allowed in this version of JSON-LD. This decision was made due to the extreme amount of added complexity when processing lists of lists.

But the importance of ordering objects doesn’t go away just because the RDF graph definitions and/or syntax makes it difficult. We can’t ignore it, and no one getting healthcare would be happy with the outcomes if we managed to get healthcare process to ignore it. The same applies to the issue with missing elements – there’s no facilty to insert default values in json-ld, let alone to do so conditionally.

So we could either

  • Complicate the json format greatly to make the json-ld RDF useful
  • Accept the simple RDF produced by json-ld and just say that all the reasoning you would want to do isn’t actually necessary
    • (or some combination of those two)
  • Or accept that there’s a transform between the regular forms of FHIR (JSON and XML which are very close) and the optimal RDF form, and concentrate on making implementations of that transform easy to use in practice

I think it’s inevitable that we’ll be going for the 3rd.

p.s. should json-ld address these issues? I think JSON-LD has to address the ‘names scoped by types’ issue, but for the rest, I don’t know. The missing element problem is ubiquitous across interfaces – elements with default values are omitted for efficiency everywhere – but there is a lot of complexity in these things. Perhaps there could be an @conversion which is a reference to a server that will convert the content to RDF instead of a @context. That’s not so nice from a client’s perspective, but it avoids specifying a huge amount of complexity in the conversion process.

p.p.s there’s further analysis about this on the FHIR wiki.

Establishing Interoperability by Legislative Fiat

h/t to Roger Maduro for the notification about the Rep Burgess Bill:

The office of Rep. Michael C. Burgess, MD (R-Texas) released a draft of the interoperability bill that they have been working for the past several months on Friday. Rep. Burgess, one of the few physicians in Congress, has been working very hard with his staff to come up with legislation that can fix the current Health IT “lock-in” crisis.

Well, I’m not sure that it’s a crisis. Perhaps it’s one politically, but maybe legislation can help. With that in mind, the centerpiece of the legislation, as far as I can see, is these 3 clauses:

‘‘(a) INTEROPERABILITY.—In order for a qualified electronic health record to be considered interoperable, such record must satisfy the following criteria:

‘‘(1) OPEN ACCESS.—The record allows authorized users access to the entirety of a patient’s data from any and all qualified electronic health records without restriction.

‘‘(2) COMPLETE ACCESS TO HEALTH DATA.— The record allows authorized users access to the en- tirety of a patient’s data in one location, without the need for multiple interfaces (such as sign on systems).

‘‘(3) DOES NOT BLOCK ACCESS TO OTHER QUALIFIED ELECTRONIC HEALTH RECORDS.—The record does not prevent end users from interfacing with other qualified electronic health records.

Well, there’s some serious issues around wording here.

Firstly, with regard to #1:

  • What’s the scope of this? a natural reading of this is that “the record’ allows access to all patient data from any institution or anywhere else. I’m pretty sure that’s what not they mean to say, but what are they saying? What ‘any and all’?
  • Presumably they do want to allow the authorizing user – the patient – to be able restrict access to their record from other authorised users. But that’s not what it says
  • The proposed bill doesn’t clarify what’s the ‘patient record’ as opposed to the institution’s record about the patient. Perhaps other legislation qualifies that, but it’s a tricky issue. Where does, for instance, a hospital record a note that clinicians should be alert for parental abuse? In the child’s record where the parent sees it?
  • Further to this, just what are health records? e.g. Are the internal process records from a diagnostic lab part of ‘any and all qualified health records’? Just how far does this go?

With regard to #2:

  • What’s an ‘interface’? As a technologist, this has so many possible meanings… so many ways that this could be interpreted.
  • I think it’s probably not a very good idea for legislation to decide on system architecture choices. In particular, this sentence is not going to mesh well with OAuth based schemes for matching patient control to institutional liability, and that’s going to be a big problem.
  • I’m also not particularly clear what ‘one location’ means. Hopefully this would not be interpreted to mean that the various servers must be co-located, but if it doesn’t, what does it mean exactly?

With regard to #3:

  • I can’t imagine how one system could block access to other qualified health records. Except by some policy exclusivity, I suppose, but I don’t know what that would be. Probably, if this was written more clearly, I’d be in agreement. But I don’t really know what it’s saying

There’s some serious omissions from this as well:

  •  There’s nothing to say that the information must be understandable – a system could put up an end-point that returned an encrypted zip file of random assorted stuff and still meet the legislation
  • There’s no mention of standards or consistency at all
  • There’s no mention of any clinical criteria as goals or assessment criteria

The last is actually significant; one of the real obstacles to interoperability is the lack of agreement between clinicians (especially across disciplines) about clinical interoperability. There’s this belief that IT is some magic bullet that will create meaningful outcomes, but that won’t happen without clinical change.

As usual, legislation is a blunt instrument, and this bill as worded would do way more damage than benefit. So is there better wording? I don’t have any off the top of my head – anything we could try to say is embedded in today’s solutions, and would prevent tomorrow’s (better) solutions.

It would be good if the legislation at least mentioned standards, though. But we’re decades away from having agreed standards that cover even 10% of the scope of “any and all qualified electronic health records”

#FHIR Terminology Services Connectathon

This week I was in Washington DC for the inaugural FHIR terminology services connectathon. This was the first of its kind: a connectathon focused on the terminology services portion of the FHIR specification.

The following organizations were represented:

  • Apelon (on behalf of openHIE)
  • VSAC/NLM
  • IMO
  • Regenstrief
  • Lantana
  • Nictiz
  • CSIRO
  • Smart Platforms
  • HL7 (through vocabulary co-chairs from MDPartners and Hausam Consulting)

The focus of the connectathon was on the two simplest operations in the terminology services API:

  • Given a value set definition, generate an expansion that contains the actual codes in the value set
  • Test whether a value set contains a code/system pair

Value Set Operations

In order to use or implement the terminology services API, the first thing to do is to understand value set expansions. Logically, a value set has 3 aspects:

  • A set of metadata that identify the value set and describe its purpose and the rules for its use
  • A “content logical definition” – the rules about what codes from what code systems are included in the value set
  • The list of actual codes in the value set (the expansion)

The key to understanding this is that content logical definition can be both complicated and/or not fully specified. The most common example is that the logical definition doesn’t fix the version of the code system, e.g. “take all procedures from SNOMED CT” – but which version of SNOMED CT? That’s delegated to the run-time environment.

This means that the process of converting from the “content logical definition” to the expansion is complicated, and is basically a job for terminology expert systems.

The FHIR terminology service API includes a very simple operation to fetch the expansion for any given value set:

GET [base]/ValueSet/[id]/$expand

This says that for the server’s value set [id], return the list of codes in the value set. It’s up to the server how it decides to generate the expansion – maybe it generates it each time based on the definition, or maybe it caches it internally and simply sends it.

Value set validation is easier – the client simply gives the server a system and a code and asks whether it’s valid in a particular value set:

GET [base]/ValueSet/[id]/$validate?system=http://loinc.org&code=2344-2

The server returns a true/false – is the code valid, and in the specified valueset? – along with human readable description of any issues detected.

Note that for these operations, there’s a number of syntactical variations to reflect the various conditions under which the operation is executed.

A key part of the first day of the connectathon was a mini-tutorial that was a detailed review of the value set resource, the expansion operation, and the way that the entire terminology service space works in the FHIR eco-system.

Connectathon Test Script

This connectathon introduced a new feature that we intend to introduce in future connectathons: a test script (view the source). The test script contains:

  • An HTML summary of the tests that a server has to pass
  • A list of setup actions to take in order to create the conditions for the test script to execute
  • A series of test cases, each of which consists of:
    • A logical FHIR interaction to perform on the server
    • Rules about the HTTP response
    • Rules about the resource that is returned

Note: The test script is actually written as if it’s a FHIR resource, though we haven’t actually defined a resource for this yet.

For the terminology services connectathon, the test script

  • Defined a series of value sets that contained different kinds of content logical definition
  • Performed a series of different expansion and validation operations, checking that the server returned the right result

For this connectathon, the terminology script could be used in the several different ways – as simply a detailed technical specification of the kind of things that were being tested by the connectathon, or as an executional test sequence using the FHIR Sprinkler tool. For this connectathon, several parties used the script, but needed to vary its content to account for differences in server functionality, and also the inner details of the various SNOMED and other code systems in use.

The test script wasn’t the only way to test – a number of participants used either their browser or the POSTman browser plug-in to test the functionality of the various servers represented at the connectathon.

Outcomes

Since this was the first connectathon, we didn’t have any particular expectations in terms of formal testing outcomes. So it was a pleasant surprise that by the second day, a number of participants were using the test script to automate testing their server for technical conformance to the specification.

In particular, the VSAC prototype FHIR server was passing all 55 tests in the test script – congratulations! (This doesn’t mean that VSAC is going to put up a FHIR server to access all the value sets as of tomorrow, but clearly that’s the end game. Of course, it will take considerable time for them to get to that point and depends on approvals, budgets etc).

That’s our desired end-point, though – that all the primary sources of value sets will make them available through a standard API, one that’s used by all the operational terminology services as well.  Really, this connectathon was initiated by the openHIE community, who are examining the FHIR terminology services API to see whether it’s suitable for adoption as their base terminology API for exactly this use. For the FHIR project, it’s our pleasure to work with openHIE, because there’s strong alignment between our two communities.

This connectathon also produced the normal outcomes that we’ve come to expect:

  • Foster continued growth of a community with strong interactions
  • Significantly increase the knowledge of the FHIR API in that community
  • Generate a series of suggestions for improvement in the FHIR specification itself

I’m really pleased with these outcomes – I’ve come to believe that the terminology space will be a significant early win for the FHIR community. With that in mind, the attendees felt that for future connectathons, it would be good to make them part of the overall FHIR connectathons held the weekend before the HL7 working group meeting. This would have several advantages, the most important of which is that we can start testing out practical ways to further the integration of terminology services into routine use of FHIR for clinical purposes. OpenHIE may also consider holding their own connectathon at some time.

At the moment, the only server that was tested at the connectathon that is publicly available is mine. It’s at http://fhir-dev.healthintersections.com.au/open, and it passes all the tests in the test script. Hopefully we’ll be able to add a couple more servers that will be available in an ongoing fashion.

 

p.s. I’m intending to make a follow up post soon about how simple the terminology service is to use in practice, but we’re pretty busy preparing the upcoming FHIR DSTU ballot.

FHIR and Healthcare Informatics Education

One of the interesting things about FHIR is how it offers new prospects for real practical hands-on education.

This is about much more than that it’s much easier and more accessible than other health informatics standards. These are the reasons why:

  • the technology base of the implementation is much more open (browsers, etc)
  • there’s a great abundance of open source tools
  • the community’s focus on examples means that there’s already lots of examples
  • the general focus on patient access to data will mean that students are much more easily able to get access to real data (their own, and others – by permission, of course)

But so far, this has remained just a prospect.

Well, not any more.

Last week, at Furore DevDays, I met Simone Heckmann, from Heilbronn, who’s doing a bunch of real interesting stuff with her students. Ewout described it here:

Simone uses FHIR to teach her students about interoperability and show them the caveats and real-life problems involved in building connected systems. And that’s only part of her teaching curriculum; in addition to have them map one type of messages to another standard, she also asks her students to select any of the available open-source FHIR clients and servers, play with them for about a month and extend them. And this is just the prelude to the final part of the teaching program: she then organizes a hackathon at the Hochschule where the students bring their pet projects they have been working on and test them against each other

This is really cool – my immediate response was the same as Ewout’s: “I want to be a student at Heilbronn“. This is really teaching students something – real experience at getting information to flow between systems.

Simone told me that she’s planning to post her course notes etc on the web. I’ll be sure to post a link to them when she does. I’m impressed – this is exactly the kind of can-do practical work that FHIR is all about.

And if that’s not enough, she’s got a FHIR blog too (it’s in German – this is the English translation) .

Welcome, Simone, to the FHIR community.